Alzheimer’s disease (AD) is an incurable degenerative disease of the central nervous system and the most common type of dementia in the elderly. Despite years of extensive research efforts, our understanding of the etiology and pathogenesis of AD is still highly limited. Nevertheless, several hypotheses related to risk factors for AD have been proposed. Moreover, plant-derived dietary polyphenols were also shown to exert protective effects against neurodegenerative diseases such as AD. In this review, we summarize the regulatory effects of the most well-known plant-derived dietary polyphenols on several AD-related molecular mechanisms, such as amelioration of oxidative stress injury, inhibition of aberrant glial cell activation to alleviate neuroinflammation, inhibition of the generation and promotion of the clearance of toxic amyloid-β (Aβ) plaques, inhibition of cholinesterase enzyme activity, and increase in acetylcholine levels in the brain. We also discuss the issue of bioavailability and the potential for improvement in this regard. This review is expected to encourage further research on the role of natural dietary plant polyphenols in the treatment of AD.

Drug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used machine learning (ML) to mine multiple microarrays and identify useful genes that could contribute to diagnosing DILI. In this prospective study, we screened six eligible microarrays from the Gene Expression Omnibus (GEO) database. First, 21 differentially expressed genes (DEGs) were identified in the training set. Subsequently, a functional enrichment analysis of the DEGs was performed. We then used six ML algorithms to identify potentially useful genes. Based on receiver operating characteristic (ROC), four genes, DDIT3, GADD45A, SLC3A2, and RBM24, were identified. The average values of the area under the curve (AUC) for these four genes were higher than 0.8 in both the training and testing sets. In addition, the results of immune cell correlation analysis showed that these four genes were highly significantly correlated with multiple immune cells. Our study revealed that DDIT3, GADD45A, SLC3A2, and RBM24 could be biomarkers contributing to the identification of patients with DILI.

Stephaniae Tetrandrae Radix (STR) is a commonly used herb with a history of thousands of years. Accumulating evidence shows the therapeutic effect on breast cancer (BC) of STR. Here, we aimed to elucidate the active components and mechanisms of STR against BC. The active components and targets were retrieved and screened from the corresponding databases. A target protein–protein interaction (PPI) network was built and Ingenuity Pathway Analysis (IPA) used to analyze and screen key targets and pathways. Subsequently, molecular docking was performed to visualize the patterns of interactions between components and targets. Finally, the main active components of STR in treating BC were confirmed by in vitro experiments, and 34 common targets were obtained. The PPI network and IPA showed that the key targets were TP53, JUN, CASP3, and so on. Additionally, signaling pathways were enriched. Docking verified that the active components have good binding potential with the key targets, especially tetrandrine (Tet) and fangchinoline (Fang). In vitro studies confirmed that they significantly inhibited the viability of MDA-MB-231 cells and increased LDH leakage rate compared to MCF-10A cells. STR participates in many cell processes and regulate multiple targets, thereby playing an anti-breast cancer role. Tet and Fang may be the main active components.